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Welcome to the main module of pySPACE, the Signal Processing And Classification Environment
in Python. For a complete and up-to date documentation of the release,
we refer to
our git project web page.

This module contains the basic imports for using pySPACE and sets the default
configuration.

Documentation

Documentation is done with Sphinx
and some helper functions coming with the software for more customization.
The folder that contains all the documentation is called docs.
To compile you first have to install Sphinx 1.1 or a better version.
The documentation can be created by running makehtml in the docs directory
(therefore we have a Makefile in the docs folder).
To view the documentation open the index.html in the .build/html folder.

Structure of the Software and First Steps

This software is structured into six main parts on the top level which give you a hint of
where you might like to go. pySPACE is a highly modular framework, so all possible
tasks and algorithms are called missions, input and output are defined
as resources.

To start the software, go to the run package, to get an idea of
what you can do, see the documentation of missions (nodes and operations)
and the documentation in Getting started, the Overview and maybe some Tutorials.

Where to Go to Integrate Your Own Extensions

If you want to integrate your own application, you will probably have to create new
missions. If your algorithm can handle single data_types
or only works with one dataset
or a combination of training and test set, you can integrate it easily by defining it in the
nodes package.
Otherwise you will have to integrate it into the
operations package, which is also a subpackage of the
missions package.
Last but not least, you may want to write only a wrapper for your algorithm and
even implement your algorithm in a different language like C++ or even Matlab.
So the wrapper is integrated as mentioned before and the real algorithm
implementation can be done in the support package.

If you should realize, that this software is unable to load, store or
process your special type of data, you should have a look into the
resources package.
Here different stages of accumulation of data are defined,
but most probably, you will be interested in
dataset_defs.
Very likely you will not have to define a complete new dataset type but
only add the functionality you need to the existing ones.

When integrating new components you should also write small
unittests in tests.

In case you need some of the tools please have a look at the
respective documentation, too. This is also true for
environments similar holds, e.g., if you want to implement
new backend
or have an own live application.